Bibliographic record
Abstract
Our world is growing older. As birth rates continue to drop, and younger residents and recent immigrants congregate in a small number of global cities, the demographic geography of Western nations has become increasing uneven (Townshend and Walker, 2015). While it is important to celebrate the fact that people are living longer and healthier, such changes in population also challenge the viability of economic and healthcare systems (Nefs et al, 2013). Canada’s demographic shift is particularly significant as Canada is home to the world’s largest proportion of ‘baby boomers’ – those born between 1947 and 1966 (Foot, 1999). As the baby boomers reach and pass retirement age, Canada’s population pyramid will become increasingly top-heavy. The shift is already well underway. As of 2015, Canadians aged 65 years and older have outnumbered children aged 0 to 14 years (Statistics Canada, 2015). The aging of the population has called into question how prepared national, provincial, and local governments are to support the needs of the heterogeneous older adult population. Though national- and provincial-level planning on macro-level issues like pensions and healthcare is commonplace, these debates neglect how policies play out on the ground in the complex and varied regional milieu of a large nation like Canada (Hodge, 2008). Recent research has shown a ubiquitous increase in older adult populations across Canadian municipalities (Hartt and Biglieri, 2018). Of course, an increase in the older adult population is not problematic in its own right. More concerning is that the Canadian cities expected to age the most are also the least likely to have begun any age-friendly planning (Hartt and Biglieri, 2018).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.445 | 0.291 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".